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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 45014510 of 9051 papers

TitleStatusHype
Natural Language Processing in Customer Service: A Systematic Review0
Scaffold-Based Multi-Objective Drug Candidate Optimization0
Detecting Bone Lesions in X-Ray Under Diverse Acquisition Conditions0
Objaverse: A Universe of Annotated 3D Objects0
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation0
Calibration-Free Driver Drowsiness Classification based on Manifold-Level AugmentationCode0
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
APOLLO: An Optimized Training Approach for Long-form Numerical ReasoningCode1
Deep Negative Correlation Classification0
Multiple Phase Transitions Shape Biodiversity of a Migrating Population0
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